Management framework for Big Data projects based on success factors and criteria
Resumo
Context: The objective of this research is to examine success management in Big Data projects. Big Data informs strategic decisionmaking, but managing these projects effectively presents challenges to delivering value for organizations. Problem: The issue addressed is the lack of a structured management model focused on success in Big Data projects to reduce failure rates. Organizations face unique challenges with Big Data, often leading to project failures. Solution: Thiswork proposes a conceptual framework combining project management and success management for Big Data projects. It was developed through a literature review and interviews with experienced professionals in the field. IS Theory: This research is grounded in success management theory from project management, which is widely used in information systems projects. This study demonstrates its relevance to Big Data projects. Method: Design Science Research (DSR) methodology guided this study, including a literature review and expert interviews to refine a framework for Big Data project success management. Summary of Results: The framework addresses gaps found in the literature and interviews, covering project planning, execution, and closure, and incorporating the specific characteristics of Big Data and Success Management. Contributions and Impact on the IS Field: This research contributes to data science by applying success management principles, offering a practical and academic framework for Big Data project management. It provides strategies for effectively managing Big Data project success.
Palavras-chave:
Big Data, Success Management, Project Management, Success Criteria, Success Factors
Referências
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C. Boscarioli, R. M. Araujo, and R. S. Maciel (Eds.). 2016. I GranDSI-BR: Grand Research Challenges in Information Systems in Brazil 2016-2026. Sociedade Brasileira de Computação (SBC). DOI: 10.5753/sbc.2884.0
P. Cato, P. Gölzer, and W. Demmelhuber. 2015. An investigation into the implementation factors affecting the success of big data systems. In 2015 11th International Conference on Innovations in Information Technology (IIT). IEEE, 134–139.
H. Chen, R. H. L. Chiang, and V. C. Storey. 2012. Business intelligence and analytics: From big data to big impact. MIS quarterly (2012), 1165–1188.
W. Chen and A. Quan-Haase. 2018. Big data ethics and politics: Toward new understandings. Social Science Computer Review v. 38, n.1 (2018), 3–9.
S. Dash, S. K. Shakyawar, M. Sharma, and S. Kaushik. 2019. Big data in healthcare: management, analysis and future prospects. Journal of big data 6, 1 (2019), 1–25.
T.H. Davenport. 2006. Competing on analytics. Harvard Business Review v. 84, n. 1 (2006), 98.
A. Errezgouny and A. Cherkaoui. 2022. Contribution in big data projects management. In EDP SCIENCES. E3S Web of Conferences. [S.l], Vol. v. 351. 01066.
J. M. Evers. 2014. Critical success factors of business intelligence and big data analysis. Tilburg University (2014).
S Eybers and M. J. Hattingh. 2017. Critical success factor categories for big data: A preliminary analysis of the current academic landscape. In 2017 IST-Africa Week Conference (IST-Africa). IEEE, 1–11.
J. Gao, A. Koronios, and S. Selle. 2015. Towards a process view on critical success factors in big data analytics projects. In Twenty-first Americas Conference on Information Systems (AMCIS), Puerto Rico.
Gartner. 2012. Glossary: Big Data. [link]
M. Ghasemaghaei and O. Turel. 2021. Possible negative effects of big data on decision quality in firms: The role of knowledge hiding behaviors. Information Systems Journal v. 31, n. 2 (2021), 268–293,.
G. Grander, L. F. da Silva, and E. D. R. S. Gonzalez. 2021. Big data as a value generator in decision support systems: A literature review. Revista de Gestão 28, 3 (2021), 205–222.
G. Grander, L. F. Da Silva, E. D. R. S. Gonzalez, and R. Penha. 2022. Framework for structuring big data projects. Electronics 11, 21 (2022), 3540.
L.A. Guion, D.C. Diehl, and D. Mcdonald. 2011. Conducting an In-Depth Interview.
A.R. Hevner, S. T. March, and J. Park. 2004. A design science research methodology for information systems research. MIS Quarterly v. 28, n. 1 (2004), 75–105.
S. Kaisler, F. Armour, J. A. Espinosa, and W. Money. 2013. Big data: Issues and challenges moving forward. In Proceedings of the Annual Hawaii International Conference on System Sciences. IEEE, 995–1004.
J. Kelly and J. Kaskade. 2013. CIOs and Big Data. [link]
M. Khan, X.Wu, X. Xu, andW. Dou. 2017. Big data challenges and opportunities in the hype of industry 4.0. In 2017 IEEE International Conference on Communications (ICC.
J. Kraimer. 2017. Who’s (re)educating our leaders in this time of digital transformation? Design Management Review v. 28, n. 3 (2017), 17–23.
B. Kuechler and V. Vaishnavi. 2008. On theory development in design science research: anatomy of a research project. European Journal of Information Systems v. 17, n. 5 (2008), 489–504.
S. Kvale and S. Brinkmann. 2009. Interviews: Learning the craft of qualitative research interviewing. sage (2009).
F. Larentis, C.P. Giacomello, and M.E. Camargo. 2012. Análise da importância em pesquisas de satisfação através da regressão múltipla: estudo do efeito de diferentes pontos de escala. In Análise–Revista de Administração da PUCRS. Vol. v. 23, n. 3. 258–269.
S.T. March and G.F. Smith. 1995. Design and natural science research on information technology. Decision support systems, Elsevier v. 15, n. 4 (1995), 251–266.
A. Oussous, F. Z. Benjelloun, A. A. Lahcen, and S. Belfkih. 2018. Big data technologies: A survey. Journal of King Saud University - Computer and Information Sciences v. 30, n. 4 (2018), 431–448.
K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee. 2007. A design science research methodology for information systems research. Journal of management information systems, Taylor Francis v. 24, n. 3 (2007), 45–77.
J. Pereira, J. Varajão, and N. Takagi. 2022. Evaluation of information systems project success – insights from practitioners. Information Systems Management v. 39, n. 2 (2022), 138–155.
O. Pesämaa, M. Bourne, M. Bosch-Rekveldt, R. Kirkham, and R. Forster. 2020. Call for papers: Performance measurement in project management. International Journal of Project Management v. 38, n. 8 (2020), 559–560.
J.K. Pinto, D.P. Slevin, and B. English. 2009. Trust in projects: An empirical assessment of owner/contractor relationships. International Journal of Project Management v. 27, n. 6 (2009), 638–648.
J.K. Pinto, D.P. Slevin, and B. English. 2021. Call for papers for special issue on project success. International Journal of Project Management v. 39 (2021), 213–215.
G. Reggio and E. Astesiano. 2020. Big-data/analytics projects failure: A literature review. , 246–255 pages.
P. Russom. 2011. Big data analytics. TDWI Research v. 38 (2011), 38–48.
J. Saltz. 2015. The need for new processes, methodologies, and tools to support big data teams and improve big data project effectiveness. In IEEE International Conference on Big Data (Big Data.
M. Y. Santos, J. O. Sá, C. Andrade, F. V. Lima, E. Costa, C. Costa, B. Martinho, and J. Galvão. 2017. A Big Data System Supporting Bosch Braga Industry 4.0 Strategy. International Journal of Information Management v. 37, n. 6 (2017), 750–760.
M. Savastano, C. Amendola, and F. D’Ascenzo. 2018. How Digital Transformation Is Reshaping The Manufacturing Industry Value Chain: The New Digital Manufacturing Ecosystem Applied To a Case Study From The Food Industry. In . Lecture Notes in Information Systems and Organisation. Springer International Publishing, 127–142.
P. Serrador and J.K. Pinto. 2015. Does agile work?—a quantitative analysis of agile project success. International Journal of Project Management v. 33, n. 5 (2015), 1040–1051.
N. Takagi. 2021. Implementing success management and PRINCE2 in a BPM public project. In Australasian Conference on Information Systems (ACIS. Association for Information Systems, Sydney, Australia.
N. Takagi and J. Varajão. 2019. Integration of success management into project management guides and methodologies - position paper. Procedia Computer Science v. 164 (2019), 366–372.
N. Takagi and J. Varajão. 2021. Success Management and Scrum for IS Projects-An Integrated Approach. PACIS, In. 46 pages.
N. Takagi and J. Varajão. 2022. ISO 21500 and Success Management: An Integrated Model for Project Management. International Journal of Quality and Reliability Management v. 39, n. 2 (2022), 408–427.
N. Takagi, J. Varajão, and P.A. Ribeiro. 2019. Integrating success management into EU PM2. In Proceedings of the Portuguese Association for Information Systems Conference. Lisbon, Portugal.
N. Takagi, J. Varajão, and T. Ventura. 2024. Implementing success management on government-to-government projects: an integrated perspective with the PMBOK guide. International Journal of Managing Projects in Business v. 17, n. 1 (2024), 153–171.
L. Tang, J. Li, H. Du, L. Li, J. Wu, and S. Wang. 2022. Big data in forecasting research: a literature review. Big Data Research, Elsevier v. 27 (2022), 1–30.
C. W. Tsai, C. F. Lai, H. C. Chao, and A. V. Vasilakos. 2015. Big data analytics: A survey. Journal of Big Data v. 2, n. 1 (2015), 1–32.
J. Varajão. 2016. Success Management as a PM knowledge area–work-in-progress. Procedia Computer Science v. 100 (2016), 1095–1102.
J. Varajão. 2018. A new process for success management – bringing order to a typically ad-hoc area. Journal of Modern Project Management v. 5, n. 3 (2018), 92–99.
J. Varajão, L. Magalhães, L. Freitas, and P. Rocha. 2022. Success Management – From theory to practice. International Journal of Project Management v. 40, n. 5 (2022), 481–498.
J. Venable. 2006. The role of theory and theorising in design science research. In CITESEER. Proceedings of the 1st International Conference on Design Science in Information Systems and Technology (DESRIST 2006. 1–18. In:.
S. Biesdorf, D. Court, and P. Willmot. 2013. Big data: What’s your plan? McKinsey Quarterly (2013). [link]
C. Bizer, P. Boncz, M. L Brodie, and O. Erling. 2012. The meaningful use of big data: four perspectives–four challenges. ACM Sigmod Record 40, 4 (2012), 56–60.
C. Boscarioli, R. M. Araujo, and R. S. Maciel (Eds.). 2016. I GranDSI-BR: Grand Research Challenges in Information Systems in Brazil 2016-2026. Sociedade Brasileira de Computação (SBC). DOI: 10.5753/sbc.2884.0
P. Cato, P. Gölzer, and W. Demmelhuber. 2015. An investigation into the implementation factors affecting the success of big data systems. In 2015 11th International Conference on Innovations in Information Technology (IIT). IEEE, 134–139.
H. Chen, R. H. L. Chiang, and V. C. Storey. 2012. Business intelligence and analytics: From big data to big impact. MIS quarterly (2012), 1165–1188.
W. Chen and A. Quan-Haase. 2018. Big data ethics and politics: Toward new understandings. Social Science Computer Review v. 38, n.1 (2018), 3–9.
S. Dash, S. K. Shakyawar, M. Sharma, and S. Kaushik. 2019. Big data in healthcare: management, analysis and future prospects. Journal of big data 6, 1 (2019), 1–25.
T.H. Davenport. 2006. Competing on analytics. Harvard Business Review v. 84, n. 1 (2006), 98.
A. Errezgouny and A. Cherkaoui. 2022. Contribution in big data projects management. In EDP SCIENCES. E3S Web of Conferences. [S.l], Vol. v. 351. 01066.
J. M. Evers. 2014. Critical success factors of business intelligence and big data analysis. Tilburg University (2014).
S Eybers and M. J. Hattingh. 2017. Critical success factor categories for big data: A preliminary analysis of the current academic landscape. In 2017 IST-Africa Week Conference (IST-Africa). IEEE, 1–11.
J. Gao, A. Koronios, and S. Selle. 2015. Towards a process view on critical success factors in big data analytics projects. In Twenty-first Americas Conference on Information Systems (AMCIS), Puerto Rico.
Gartner. 2012. Glossary: Big Data. [link]
M. Ghasemaghaei and O. Turel. 2021. Possible negative effects of big data on decision quality in firms: The role of knowledge hiding behaviors. Information Systems Journal v. 31, n. 2 (2021), 268–293,.
G. Grander, L. F. da Silva, and E. D. R. S. Gonzalez. 2021. Big data as a value generator in decision support systems: A literature review. Revista de Gestão 28, 3 (2021), 205–222.
G. Grander, L. F. Da Silva, E. D. R. S. Gonzalez, and R. Penha. 2022. Framework for structuring big data projects. Electronics 11, 21 (2022), 3540.
L.A. Guion, D.C. Diehl, and D. Mcdonald. 2011. Conducting an In-Depth Interview.
A.R. Hevner, S. T. March, and J. Park. 2004. A design science research methodology for information systems research. MIS Quarterly v. 28, n. 1 (2004), 75–105.
S. Kaisler, F. Armour, J. A. Espinosa, and W. Money. 2013. Big data: Issues and challenges moving forward. In Proceedings of the Annual Hawaii International Conference on System Sciences. IEEE, 995–1004.
J. Kelly and J. Kaskade. 2013. CIOs and Big Data. [link]
M. Khan, X.Wu, X. Xu, andW. Dou. 2017. Big data challenges and opportunities in the hype of industry 4.0. In 2017 IEEE International Conference on Communications (ICC.
J. Kraimer. 2017. Who’s (re)educating our leaders in this time of digital transformation? Design Management Review v. 28, n. 3 (2017), 17–23.
B. Kuechler and V. Vaishnavi. 2008. On theory development in design science research: anatomy of a research project. European Journal of Information Systems v. 17, n. 5 (2008), 489–504.
S. Kvale and S. Brinkmann. 2009. Interviews: Learning the craft of qualitative research interviewing. sage (2009).
F. Larentis, C.P. Giacomello, and M.E. Camargo. 2012. Análise da importância em pesquisas de satisfação através da regressão múltipla: estudo do efeito de diferentes pontos de escala. In Análise–Revista de Administração da PUCRS. Vol. v. 23, n. 3. 258–269.
S.T. March and G.F. Smith. 1995. Design and natural science research on information technology. Decision support systems, Elsevier v. 15, n. 4 (1995), 251–266.
A. Oussous, F. Z. Benjelloun, A. A. Lahcen, and S. Belfkih. 2018. Big data technologies: A survey. Journal of King Saud University - Computer and Information Sciences v. 30, n. 4 (2018), 431–448.
K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee. 2007. A design science research methodology for information systems research. Journal of management information systems, Taylor Francis v. 24, n. 3 (2007), 45–77.
J. Pereira, J. Varajão, and N. Takagi. 2022. Evaluation of information systems project success – insights from practitioners. Information Systems Management v. 39, n. 2 (2022), 138–155.
O. Pesämaa, M. Bourne, M. Bosch-Rekveldt, R. Kirkham, and R. Forster. 2020. Call for papers: Performance measurement in project management. International Journal of Project Management v. 38, n. 8 (2020), 559–560.
J.K. Pinto, D.P. Slevin, and B. English. 2009. Trust in projects: An empirical assessment of owner/contractor relationships. International Journal of Project Management v. 27, n. 6 (2009), 638–648.
J.K. Pinto, D.P. Slevin, and B. English. 2021. Call for papers for special issue on project success. International Journal of Project Management v. 39 (2021), 213–215.
G. Reggio and E. Astesiano. 2020. Big-data/analytics projects failure: A literature review. , 246–255 pages.
P. Russom. 2011. Big data analytics. TDWI Research v. 38 (2011), 38–48.
J. Saltz. 2015. The need for new processes, methodologies, and tools to support big data teams and improve big data project effectiveness. In IEEE International Conference on Big Data (Big Data.
M. Y. Santos, J. O. Sá, C. Andrade, F. V. Lima, E. Costa, C. Costa, B. Martinho, and J. Galvão. 2017. A Big Data System Supporting Bosch Braga Industry 4.0 Strategy. International Journal of Information Management v. 37, n. 6 (2017), 750–760.
M. Savastano, C. Amendola, and F. D’Ascenzo. 2018. How Digital Transformation Is Reshaping The Manufacturing Industry Value Chain: The New Digital Manufacturing Ecosystem Applied To a Case Study From The Food Industry. In . Lecture Notes in Information Systems and Organisation. Springer International Publishing, 127–142.
P. Serrador and J.K. Pinto. 2015. Does agile work?—a quantitative analysis of agile project success. International Journal of Project Management v. 33, n. 5 (2015), 1040–1051.
N. Takagi. 2021. Implementing success management and PRINCE2 in a BPM public project. In Australasian Conference on Information Systems (ACIS. Association for Information Systems, Sydney, Australia.
N. Takagi and J. Varajão. 2019. Integration of success management into project management guides and methodologies - position paper. Procedia Computer Science v. 164 (2019), 366–372.
N. Takagi and J. Varajão. 2021. Success Management and Scrum for IS Projects-An Integrated Approach. PACIS, In. 46 pages.
N. Takagi and J. Varajão. 2022. ISO 21500 and Success Management: An Integrated Model for Project Management. International Journal of Quality and Reliability Management v. 39, n. 2 (2022), 408–427.
N. Takagi, J. Varajão, and P.A. Ribeiro. 2019. Integrating success management into EU PM2. In Proceedings of the Portuguese Association for Information Systems Conference. Lisbon, Portugal.
N. Takagi, J. Varajão, and T. Ventura. 2024. Implementing success management on government-to-government projects: an integrated perspective with the PMBOK guide. International Journal of Managing Projects in Business v. 17, n. 1 (2024), 153–171.
L. Tang, J. Li, H. Du, L. Li, J. Wu, and S. Wang. 2022. Big data in forecasting research: a literature review. Big Data Research, Elsevier v. 27 (2022), 1–30.
C. W. Tsai, C. F. Lai, H. C. Chao, and A. V. Vasilakos. 2015. Big data analytics: A survey. Journal of Big Data v. 2, n. 1 (2015), 1–32.
J. Varajão. 2016. Success Management as a PM knowledge area–work-in-progress. Procedia Computer Science v. 100 (2016), 1095–1102.
J. Varajão. 2018. A new process for success management – bringing order to a typically ad-hoc area. Journal of Modern Project Management v. 5, n. 3 (2018), 92–99.
J. Varajão, L. Magalhães, L. Freitas, and P. Rocha. 2022. Success Management – From theory to practice. International Journal of Project Management v. 40, n. 5 (2022), 481–498.
J. Venable. 2006. The role of theory and theorising in design science research. In CITESEER. Proceedings of the 1st International Conference on Design Science in Information Systems and Technology (DESRIST 2006. 1–18. In:.
Publicado
19/05/2025
Como Citar
SOUZA E SILVA, Karla de; TAKAGI, Nilton Hideki; OLIVEIRA, Allan Gonçalves de.
Management framework for Big Data projects based on success factors and criteria. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 21. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 329-338.
DOI: https://doi.org/10.5753/sbsi.2025.246485.
